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Article

An Efficient FPGA-Based Convolutional Neural Network for Classification: Ad-MobileNet

1
Laboratoire de Microélectronique et Instrumentation, Faculté des Sciences de Monastir, Université de Monastir, Monastir 5019, Tunisia
2
Electronics and Microelectronics Unit (SEMi), University of Mons, 7000 Mons, Belgium
3
Ecole Nationale d’Ingénieurs de Sousse, Université de Sousse, Sousse 4000, Tunisia
4
Institut Supérieur des Sciences Appliquées et de Technologie de Sousse, Université de Sousse, Sousse 4003, Tunisia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Joo-Young Kim
Electronics 2021, 10(18), 2272; https://doi.org/10.3390/electronics10182272
Received: 31 July 2021 / Revised: 7 September 2021 / Accepted: 9 September 2021 / Published: 16 September 2021
(This article belongs to the Special Issue Advanced AI Hardware Designs Based on FPGAs)
Convolutional Neural Networks (CNN) continue to dominate research in the area of hardware acceleration using Field Programmable Gate Arrays (FPGA), proving its effectiveness in a variety of computer vision applications such as object segmentation, image classification, face detection, and traffic signs recognition, among others. However, there are numerous constraints for deploying CNNs on FPGA, including limited on-chip memory, CNN size, and configuration parameters. This paper introduces Ad-MobileNet, an advanced CNN model inspired by the baseline MobileNet model. The proposed model uses an Ad-depth engine, which is an improved version of the depth-wise separable convolution unit. Moreover, we propose an FPGA-based implementation model that supports the Mish, TanhExp, and ReLU activation functions. The experimental results using the CIFAR-10 dataset show that our Ad-MobileNet has a classification accuracy of 88.76% while requiring little computational hardware resources. Compared to state-of-the-art methods, our proposed method has a fairly high recognition rate while using fewer computational hardware resources. Indeed, the proposed model helps to reduce hardware resources by more than 41% compared to that of the baseline model. View Full-Text
Keywords: FPGA; MobileNet; depthwise separable convolution; CNN; deep learning FPGA; MobileNet; depthwise separable convolution; CNN; deep learning
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MDPI and ACS Style

Bouguezzi, S.; Fredj, H.B.; Belabed, T.; Valderrama, C.; Faiedh, H.; Souani, C. An Efficient FPGA-Based Convolutional Neural Network for Classification: Ad-MobileNet. Electronics 2021, 10, 2272. https://doi.org/10.3390/electronics10182272

AMA Style

Bouguezzi S, Fredj HB, Belabed T, Valderrama C, Faiedh H, Souani C. An Efficient FPGA-Based Convolutional Neural Network for Classification: Ad-MobileNet. Electronics. 2021; 10(18):2272. https://doi.org/10.3390/electronics10182272

Chicago/Turabian Style

Bouguezzi, Safa, Hana B. Fredj, Tarek Belabed, Carlos Valderrama, Hassene Faiedh, and Chokri Souani. 2021. "An Efficient FPGA-Based Convolutional Neural Network for Classification: Ad-MobileNet" Electronics 10, no. 18: 2272. https://doi.org/10.3390/electronics10182272

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